Deep Reinforcement Learning(DRL)
Deep Reinforcement Learning with VizDoomFirst Person Shooter
... Authors of [12] combined PER with Double Q-learning and Snapshot En- sembling and tested their agent in VizDoom Defend The Center scenario. The authors train enemy detector and Q-function in a joint manner; this ...
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Deep Reinforcement Learning of the Model Fusion with Double Q learning
... intensive learning, and the use of these data for sequential training, and the neural network is ...Different DRL (deep reinforcement learning) uses different convolutional neural ...
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Learning how to Active Learn: A Deep Reinforcement Learning Approach
... Deep reinforcement learning (DRL) is a general-purpose framework for decision mak- ing based on representation ...include deep Q- learning (Mnih et al., 2015), deep ...
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Road tracking using deep reinforcement learning for self driving car applications
... objects. Reinforcement learning (RL), in a nutshell, is concerned with an agent interacting with the environment, learning an optimal policy, by trial and error, for sequential decision making ...of ...
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Deep Reinforcement Learning for Green Security Games with Real-Time Information
... novel deep reinforcement learning-based algo- rithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding ...use Deep Q-Learning ...
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Deep Reinforcement Learning for Swarm Systems
... in deep reinforcement learning for swarms and multi-agent systems in ...many-agent reinforcement learning platform based on a multi-channel image state representation, which uses ...
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Experience Selection in Deep Reinforcement Learning for Control
... off-policy reinforcement-learning algorithms can learn from samples ob- tained by a different policy than the optimal policy that is being learned, the reality of deep reinforcement ...
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A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
... novel deep reinforcement learn- ing framework for incentivizing users to rebalance such sys- ...novel deep reinforcement learning al- gorithm called Hierarchical Reinforcement ...
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Flow: Deep Reinforcement Learning for Control in SUMO
... solving deep reinforcement problems in traffic that leverages the open-source microsimulator SUMO ...use deep reinforcement learning to develop controllers for a number of intelligent ...
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Deep Reinforcement Learning for Chinese Zero Pronoun Resolution
... a deep rein- forcement learning model for anaphoric zero pro- noun ...policy-based deep reinforcement learning al- gorithm is applied to learn the policy of making coreference decisions ...
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Deep Reinforcement Learning with a Natural Language Action Space
... applying deep reinforcement learning to a variety problems, but only a few studies address problems with nat- ural language state or action ...processing, reinforcement learning has ...
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Paraphrase Generation with Deep Reinforcement Learning
... Neural paraphrase generation recently draws at- tention in different application scenarios. The task is often formalized as a sequence-to-sequence (Seq2Seq) learning problem. Prakash et al. (2016) employ a stacked ...
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Transfer in Deep Reinforcement Learning Using Knowledge Graphs
... the deep Q-network for a different game within in the same ...art DRL agents cannot complete real games, this makes the agent more effective at the source ...the deep Q-network trained on the source ...
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Deep Reinforcement Learning for Interactive Narrative Planning.
... machine learning problems utilizing sequence data (Dietterich, 2002), deep learning methods offer an especially effective set of models in solving the simulated player modeling problem with sequence ...
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Exploring Deep Reinforcement Learning with Multi Q Learning
... duced as the training progressed (Figure 2(c)) due to the decreased amount of explo- ratory moves taken. Figure 2(d) shows the value estimates of three Multi Q-learning algorithms using the same environment ...
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Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query based summarisation
... The features chosen are such that the global policy has information about the candidate sen- tence (1), the entire list of candidate sentences (2), the summary that has been generated so far (3), the input sentences that ...
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Deep Reinforcement Learning for Mention Ranking Coreference Models
... Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. Conll-2012 shared task: Modeling multilingual unrestricted coref- erence in ontonotes. In Proceedings of the Joint Con- ference on ...
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Multi-Task Deep Reinforcement Learning with PopArt
... Multi-task learning, as considered in this paper, where we get to execute, in parallel, the policies learned for each task, has potential additional benefits, including deep exploration (Osband et ...By ...
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Learning Resource Allocation and Pricing for Cloud Profit Maximization
... model-free Deep Reinforcement Learning (DRL) to capture dynamics of cloud users and better characterize inherent connections between an optimal alloca- tion/pricing policy and the states of ...
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Deep Reinforcement Learning for Drone Delivery
... of deep RL for training a drone to fly to a destination in a neighborhood environment with plenty of ...The deep RL solution is based on double deep Q-network (DDQN) [11], an extension of deep ...
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